Engineering

This course provides research training for exchange students. Students work on a research project under the guidance of assigned faculty members. Through a full-time commitment, students improve their research skills by participating in the different phases of research, including development of research plans, proposals, data analysis, and presentation of research results. A pass/no pass grade is assigned based a progress report, self-evaluation, midterm report, presentation, and final report.

This course provides individual research training for students in the Junior Year Engineering Program through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory with the consent of the faculty member in charge. They participate in various group activities, including seminars, for the purposes of training in research methods and developing teamwork skills. The specific topic studied depends on the instructor in charge of the laboratory to which each student is assigned. The methods of assessment vary with the student's project and laboratory instructor. Students submit an abstract concerning the results of their individual research each semester and present the results near the end of the program.

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by permission of the instructor. The objective of the course is to present the most effective techniques for the solution of complex decisional problems arising in the optimal planning and management of large-scale systems concerning both the public and the private sectors. Mathematical models and heuristic algorithms for the practical solution of the corresponding optimization problems are described. Particular attention is given to the algorithmic and implementation aspects. Applications of the proposed techniques to real-world problems are presented and analyzed. The course discusses topics including: basic integer programming optimization: integer programming models, formulations, relaxations; basic heuristic approaches: constructive algorithms and local search procedures, examples for KP01 and TSP; worst-case performance analysis; metaheuristics: Multistart, Tabu Search, Simulated Annealing, Genetic Algorithms, Iterated Local Search, Variable Neighborhood Search, Large Neighborhood Search, Ruin and Recreate, and Ant Systems; optimization on graphs: shortest path, minimum spanning tree, and maximum flow; heuristic and metaheuristic algorithms for difficult combinatorial optimization problems; and real-world applications. Prerequisites for this course are: basic knowledge of Operations Research, as well as the implementation of computer codes and complexity theory.

Design is often regarded as the central creative activity of engineering. This course develops a foundation for the skills of analysis, synthesis, and communication required to develop solutions to open-ended problems. It focuses on three things: (1) understanding an engineering problem, (2) finding a solution to it, (3) communicating that solution to others. This course is predominantly taught through interactive team-based design studio sessions with support from lectures on topics including the philosophy, history, and ethics of engineering design. A series of group activities with mini assessments will cover key skills like research, problem solving, and the graphic, verbal, or written communication of engineering concepts.

This special lab course nurtures international students' creative competency by offering them opportunities for learning in communities of research practice. The student's supervisor arranges the research topic. Students give three oral presentations during the study period. In the presentations, students integrate ideas and analyses on laboratory results into creative and academically coherent work. FrontierLab program coordinators and supervisors attend and evaluate the final oral presentation.

This course offers a study of concepts, definitions, and applications of nonlinear dynamics. Topics include: dimensional systems and bifurcations; systems in two-dimensions-- analysis in phase space, limited cycles, and their bifurcations; Lorenz equations and chaos; 1-D maps and route to chaos by period doubling, renormalization; fractals and strange attractors.

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by consent of the instructor. The course is divided in two modules. The aim of the first module is to provide knowledge about vehicle dynamics. Theoretical and numerical approaches are discussed to this end, as tools that allow students to predict the performance of cars in terms of longitudinal dynamics, lateral dynamics, handling, comfort, and stability. The aim of the second module is to provide the theoretical basis and the practical skills required to design embedded hardware and firmware compliant with industrial standards (safety, interoperability, maintainability). In addition, model-based design and automatic code generation using Matlab/Simulink is considered.

This course provides an introduction to the Social Study of Science and Technology. This is an area in which Edinburgh has longstanding strengths and which the course draws upon. The course examines some of the different ways of analyzing and understanding technology in society. It explores both the consequences of technical innovation for society and the ways technology is itself shaped by cultural, economic, political, and organizational factors. Students learn about a range of analytic perspectives on Technology in Society - drawing upon history, economics, and the sociologies of work, gender, and science & technology themselves. Students explore these issues in various settings - at work and in everyday life and in developing as well as developed countries. In the second part of the course, students apply these perspectives to particular technologies or issues, working together in student-centered learning. 

The course contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world. The students learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning. This course covers practical contents of statistics, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

This course emphasizes hands-on laboratory experience and teaches students research background, relevant theories, and basic laboratory techniques relevant to their field of study. Students formulate a research plan, implement it by conducting experiment-based research, and convey the results in scholarly presentations. Students submit a written research report at the end of the course.